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app.py

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+import streamlit as st
+import torch
+import pickle
+
+# Load the saved model on the CPU
+model = torch.load('saved_model.pth', map_location=torch.device('cpu'))
+
+# Load the saved tokenizer
+with open('tokenizer.pkl', 'rb') as f:
+    tokenizer = pickle.load(f)
+
+st.title("Text Classification Streamlit App")
+
+input_text = st.text_input("Enter text:")
+if st.button("Predict"):
+    with torch.no_grad():
+        inputs = tokenizer(input_text, return_tensors="pt")
+        logits = model(**inputs).logits
+        predicted_class = torch.argmax(logits, dim=1).item()
+
+        st.write(f"Predicted Class: {predicted_class}")

requirements.txt

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+torch
+transformers
+